class_docstr: A blend between TransformedLoader and LambdaLoader: stores the whole loader
  in memory, but recomputes it from scratch every epoch, instead of just once
  at initialization.
code: def __next__(self):
      try:
          return next(self.loader)
      except StopIteration as e:
          self.data_loader = self.compute_loader()
          self.loader = iter(self.data_loader)
          raise StopIteration
  
      return self.func(im, targ)
  
imports: import argparse
  from .None import cifar_models
  from .tools import folder
  import os
  import shutil
  import time
  import numpy
  import torch
  import torch.utils.data
  from torch.utils.data import DataLoader
  from torch.utils.data import Subset
  import torchvision.transforms
  from torch.utils.data import DataLoader
  from .None import imagenet_models
label_desc: Use image node_id 6 for calling the PerEpochLoader obj's underlying member method code with example usage: obj.__next__() and returns: self, next